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Given the distributed nature, detecting and defending against the backdoor attack under federated learning (FL) systems is challenging.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2016
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Fat: Federated adversarial training
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, and Beat Buesser · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
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Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2019
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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H. Brendan McMahan · 2019
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Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta · 2019
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The limitations of federated learning in sybil settings
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh · 2020
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Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy
Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro · 2020
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Secure and fault tolerant decentralized learning
Saurav Prakash, Hanieh Hashemi, Yongqin Wang, Murali Annavaram, and Salman Avestimehr · 2020
Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2021
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Sageflow: Robust federated learning against both stragglers and adversaries
Jungwuk Park, Dong-Jun Han, Minseok Choi, and Jaekyun Moon · 2021
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Bygars: Byzantine sgd with arbitrary number of attackers
Jayanth Regatti, Hao Chen, and Abhishek Gupta · 2021
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Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective
Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, and Hai Li · 2021
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A reputation mechanism is all you need: Collaborative fairness and adversarial robustness in federated learning
Xinyi Xu and Lingjuan Lyu · 2021
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Data poisoning attacks against federated learning systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu · 2020
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idlg: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Federated learning for the internet of things: Applications, challenges, and opportunities
Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, and Salman Avestimehr · 2021
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Learning to attack federated learning: A model-based reinforcement learning attack framework
Henger Li, Xiaolin Sun, and Zizhan Zheng · 2022
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Fedaudio: A federated learning benchmark for audio tasks
Tuo Zhang, Tiantian Feng, Samiul Alam, Sunwoo Lee, Mi Zhang, Shrikanth S Narayanan, and Salman Avestimehr · 2022
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